Quantitative Predictions Orchestrate Visual Signaling in Drosophila

Dendrite architecture determines mitochondrial distribution patterns in vivo

Neuronal morphology influences synaptic connectivity and neuronal signal processing. However, it remains unclear how neuronal shape affects steady-state distributions of organelles like mitochondria. In this work, we investigated the link between mitochondrial transport and dendrite branching patterns by combining mathematical modeling with in vivo measurements of dendrite architecture, mitochondrial motility, and mitochondrial localization patterns in Drosophila HS (horizontal system) neurons. In our model, different forms of morphological and transport scaling rules-which set the relative thicknesses of parent and daughter branches at each junction in the dendritic arbor and link mitochondrial motility to branch thickness-predict dramatically different global mitochondrial localization patterns. We show that HS dendrites obey the specific subset of scaling rules that, in our model, lead to realistic mitochondrial distributions. Moreover, we demonstrate that neuronal activity does not affect mitochondrial transport or localization, indicating that steady-state mitochondrial distributions are hard-wired by the architecture of the neuron.

Motor neurons generate pose-targeted movements via proprioceptive sculpting

Motor neurons are the final common pathway1 through which the brain controls movement of the body, forming the basic elements from which all movement is composed. Yet how a single motor neuron contributes to control during natural movement remains unclear. Here we anatomically and functionally characterize the individual roles of the motor neurons that control head movement in the fly, Drosophila melanogaster. Counterintuitively, we find that activity in a single motor neuron rotates the head in different directions, depending on the starting posture of the head, such that the head converges towards a pose determined by the identity of the stimulated motor neuron. A feedback model predicts that this convergent behaviour results from motor neuron drive interacting with proprioceptive feedback. We identify and genetically2 suppress a single class of proprioceptive neuron3 that changes the motor neuron-induced convergence as predicted by the feedback model. These data suggest a framework for how the brain controls movements: instead of directly generating movement in a given direction by activating a fixed set of motor neurons, the brain controls movements by adding bias to a continuing proprioceptive-motor loop.

Mapping the neural dynamics of locomotion across the Drosophila brain

Locomotion engages widely distributed networks of neurons. However, our understanding of the spatial architecture and temporal dynamics of the networks that underpin walking remains incomplete. We use volumetric two-photon imaging to map neural activity associated with walking across the entire brain of Drosophila. We define spatially clustered neural signals selectively associated with changes in either forward or angular velocity, demonstrating that neurons with similar behavioral selectivity are clustered. These signals reveal distinct topographic maps in diverse brain regions involved in navigation, memory, sensory processing, and motor control, as well as regions not previously linked to locomotion. We identify temporal trajectories of neural activity that sweep across these maps, including signals that anticipate future movement, representing the sequential engagement of clusters with different behavioral specificities. Finally, we register these maps to a connectome and identify neural networks that we propose underlie the observed signals, setting a foundation for subsequent circuit dissection. Overall, our work suggests a spatiotemporal framework for the emergence and execution of complex walking maneuvers and links this brain-wide neural activity to single neurons and local circuits.

Hummingbirds use distinct control strategies for forward and hovering flight

The detection of optic flow is important for generating optomotor responses to mediate retinal image stabilization, and it can also be used during ongoing locomotion for centring and velocity control. Previous work in hummingbirds has separately examined the roles of optic flow during hovering and when centring through a narrow passage during forward flight. To develop a hypothesis for the visual control of forward flight velocity, we examined the behaviour of hummingbirds in a flight tunnel where optic flow could be systematically manipulated. In all treatments, the animals exhibited periods of forward flight interspersed with bouts of spontaneous hovering. Hummingbirds flew fastest when they had a reliable signal of optic flow. All optic flow manipulations caused slower flight, suggesting that hummingbirds had an expected optic flow magnitude that was disrupted. In addition, upward and downward optic flow drove optomotor responses for maintaining altitude during forward flight. When hummingbirds made voluntary transitions to hovering, optomotor responses were observed to all directions. Collectively, these results are consistent with hummingbirds controlling flight speed via mechanisms that use an internal forward model to predict expected optic flow whereas flight altitude and hovering position are controlled more directly by sensory feedback from the environment.

Drosophila flying in augmented reality reveals the vision-based control autonomy of the optomotor response

For walking, swimming, and flying animals, the optomotor response is essential to stabilize gaze. How flexible is the optomotor response? Classic work in Drosophila has argued that flies adapt flight control under augmented visual feedback conditions during goal-directed bar fixation. However, whether the lower-level, reflexive optomotor response can similarly adapt to augmented visual feedback (partially autonomous) or not (autonomous) over long timescales is poorly understood. To address this question, we developed an augmented reality paradigm to study the vision-based control autonomy of the yaw optomotor response of flying fruit flies (Drosophila). Flies were placed in a flight simulator, which permitted free body rotation about the yaw axis. By feeding back body movements in real time to a visual display, we augmented and inverted visual feedback. Thus, this experimental paradigm caused a constant visual error between expected and actual visual feedback to study potential adaptive visuomotor control. By combining experiments with control theory, we demonstrate that the optomotor response is autonomous during augmented reality flight bouts of up to 30 min, which exceeds the reported learning epoch during bar fixation. Agreement between predictions from linear systems theory and experimental data supports the notion that the optomotor response is approximately linear and time invariant within our experimental assay. Even under positive visual feedback, which revealed the stability limit of flies in augmented reality, the optomotor response was autonomous. Our results support a hierarchical motor control architecture in flies with fast and autonomous reflexes at the bottom and more flexible behavior at higher levels.

Neuronal calcium spikes enable vector inversion in the Drosophila brain

A typical neuron signals to downstream cells when it is depolarized and firing sodium spikes. Some neurons, however, also fire calcium spikes when hyperpolarized. The function of such bidirectional signaling remains unclear in most circuits. Here we show how a neuron class that participates in vector computation in the fly central complex employs hyperpolarization-elicited calcium spikes to invert two-dimensional mathematical vectors. When cells switch from firing sodium to calcium spikes, this leads to a ~180° realignment between the vector encoded in the neuronal population and the fly's internal heading signal, thus inverting the vector. We show that the calcium spikes rely on the T-type calcium channel Ca-α1T, and argue, via analytical and experimental approaches, that these spikes enable vector computations in portions of angular space that would otherwise be inaccessible. These results reveal a seamless interaction between molecular, cellular and circuit properties for implementing vector math in the brain.

Neural mechanisms to incorporate visual counterevidence in self-movement estimation

In selecting appropriate behaviors, animals should weigh sensory evidence both for and against specific beliefs about the world. For instance, animals measure optic flow to estimate and control their own rotation. However, existing models of flow detection can be spuriously triggered by visual motion created by objects moving in the world. Here, we show that stationary patterns on the retina, which constitute evidence against observer rotation, suppress inappropriate stabilizing rotational behavior in the fruit fly Drosophila. In silico experiments show that artificial neural networks (ANNs) that are optimized to distinguish observer movement from external object motion similarly detect stationarity and incorporate negative evidence. Employing neural measurements and genetic manipulations, we identified components of the circuitry for stationary pattern detection, which runs parallel to the fly's local motion and optic-flow detectors. Our results show how the fly brain incorporates negative evidence to improve heading stability, exemplifying how a compact brain exploits geometrical constraints of the visual world.

Multilevel visual motion opponency in Drosophila

Inhibitory interactions between opponent neuronal pathways constitute a common circuit motif across brain areas and species. However, in most cases, synaptic wiring and biophysical, cellular and network mechanisms generating opponency are unknown. Here, we combine optogenetics, voltage and calcium imaging, connectomics, electrophysiology and modeling to reveal multilevel opponent inhibition in the fly visual system. We uncover a circuit architecture in which a single cell type implements direction-selective, motion-opponent inhibition at all three network levels. This inhibition, mediated by GluClα receptors, is balanced with excitation in strength, despite tenfold fewer synapses. The different opponent network levels constitute a nested, hierarchical structure operating at increasing spatiotemporal scales. Electrophysiology and modeling suggest that distributing this computation over consecutive network levels counteracts a reduction in gain, which would result from integrating large opposing conductances at a single instance. We propose that this neural architecture provides resilience to noise while enabling high selectivity for relevant sensory information.

Cholinergic neurons trigger epithelial Ca2+ currents to heal the gut

A fundamental and unresolved question in regenerative biology is how tissues return to homeostasis after injury. Answering this question is essential for understanding the aetiology of chronic disorders such as inflammatory bowel diseases and cancer1. We used the Drosophila midgut2 to investigate this and discovered that during regeneration a subpopulation of cholinergic3 neurons triggers Ca2+ currents among intestinal epithelial cells, the enterocytes, to promote return to homeostasis. We found that downregulation of the conserved cholinergic enzyme acetylcholinesterase4 in the gut epithelium enables acetylcholine from specific Egr5 (TNF in mammals)-sensing cholinergic neurons to activate nicotinic receptors in innervated enterocytes. This activation triggers high Ca2+, which spreads in the epithelium through Innexin2-Innexin7 gap junctions6, promoting enterocyte maturation followed by reduction of proliferation and inflammation. Disrupting this process causes chronic injury consisting of ion imbalance, Yki (YAP in humans) activation7, cell death and increase of inflammatory cytokines reminiscent of inflammatory bowel diseases8. Altogether, the conserved cholinergic pathway facilitates epithelial Ca2+ currents that heal the intestinal epithelium. Our findings demonstrate nerve- and bioelectric9-dependent intestinal regeneration and advance our current understanding of how a tissue returns to homeostasis after injury.

A comprehensive neuroanatomical survey of the Drosophila Lobula Plate Tangential Neurons with predictions for their optic flow sensitivity

Flying insects exhibit remarkable navigational abilities controlled by their compact nervous systems. Optic flow, the pattern of changes in the visual scene induced by locomotion, is a crucial sensory cue for robust self-motion estimation, especially during rapid flight. Neurons that respond to specific, large-field optic flow patterns have been studied for decades, primarily in large flies, such as houseflies, blowflies, and hover flies. The best-known optic-flow sensitive neurons are the large tangential cells of the dipteran lobula plate, whose visual-motion responses, and to a lesser extent, their morphology, have been explored using single-neuron neurophysiology. Most of these studies have focused on the large, Horizontal and Vertical System neurons, yet the lobula plate houses a much larger set of 'optic-flow' sensitive neurons, many of which have been challenging to unambiguously identify or to reliably target for functional studies. Here we report the comprehensive reconstruction and identification of the Lobula Plate Tangential Neurons in an Electron Microscopy (EM) volume of a whole Drosophila brain. This catalog of 58 LPT neurons (per brain hemisphere) contains many neurons that are described here for the first time and provides a basis for systematic investigation of the circuitry linking self-motion to locomotion control. Leveraging computational anatomy methods, we estimated the visual motion receptive fields of these neurons and compared their tuning to the visual consequence of body rotations and translational movements. We also matched these neurons, in most cases on a one-for-one basis, to stochastically labeled cells in genetic driver lines, to the mirror-symmetric neurons in the same EM brain volume, and to neurons in an additional EM data set. Using cell matches across data sets, we analyzed the integration of optic flow patterns by neurons downstream of the LPTs and find that most central brain neurons establish sharper selectivity for global optic flow patterns than their input neurons. Furthermore, we found that self-motion information extracted from optic flow is processed in distinct regions of the central brain, pointing to diverse foci for the generation of visual behaviors.

Multilevel visuomotor control of locomotion in Drosophila

Vision is critical for the control of locomotion, but the underlying neural mechanisms by which visuomotor circuits contribute to the movement of the body through space are yet not well understood. Locomotion engages multiple control systems, forming distinct interacting "control levels" driven by the activity of distributed and overlapping circuits. Therefore, a comprehensive understanding of the mechanisms underlying locomotion control requires the consideration of all control levels and their necessary coordination. Due to their small size and the wide availability of experimental tools, Drosophila has become an important model system to study this coordination. Traditionally, insect locomotion has been divided into studying either the biomechanics and local control of limbs, or navigation and course control. However, recent developments in tracking techniques, and physiological and genetic tools in Drosophila have prompted researchers to examine multilevel control coordination in flight and walking.

Long-timescale anti-directional rotation in Drosophila optomotor behavior

Locomotor movements cause visual images to be displaced across the eye, a retinal slip that is counteracted by stabilizing reflexes in many animals. In insects, optomotor turning causes the animal to turn in the direction of rotating visual stimuli, thereby reducing retinal slip and stabilizing trajectories through the world. This behavior has formed the basis for extensive dissections of motion vision. Here, we report that under certain stimulus conditions, two Drosophila species, including the widely studied Drosophila melanogaster, can suppress and even reverse the optomotor turning response over several seconds. Such 'anti-directional turning' is most strongly evoked by long-lasting, high-contrast, slow-moving visual stimuli that are distinct from those that promote syn-directional optomotor turning. Anti-directional turning, like the syn-directional optomotor response, requires the local motion detecting neurons T4 and T5. A subset of lobula plate tangential cells, CH cells, show involvement in these responses. Imaging from a variety of direction-selective cells in the lobula plate shows no evidence of dynamics that match the behavior, suggesting that the observed inversion in turning direction emerges downstream of the lobula plate. Further, anti-directional turning declines with age and exposure to light. These results show that Drosophila optomotor turning behaviors contain rich, stimulus-dependent dynamics that are inconsistent with simple reflexive stabilization responses.

The spatial and temporal structure of neural activity across the fly brain

What are the spatial and temporal scales of brainwide neuronal activity? We used swept, confocally-aligned planar excitation (SCAPE) microscopy to image all cells in a large volume of the brain of adult Drosophila with high spatiotemporal resolution while flies engaged in a variety of spontaneous behaviors. This revealed neural representations of behavior on multiple spatial and temporal scales. The activity of most neurons correlated (or anticorrelated) with running and flailing over timescales that ranged from seconds to a minute. Grooming elicited a weaker global response. Significant residual activity not directly correlated with behavior was high dimensional and reflected the activity of small clusters of spatially organized neurons that may correspond to genetically defined cell types. These clusters participate in the global dynamics, indicating that neural activity reflects a combination of local and broadly distributed components. This suggests that microcircuits with highly specified functions are provided with knowledge of the larger context in which they operate.

Epithelial Ca2+ waves triggered by enteric neurons heal the gut

A fundamental and unresolved question in regenerative biology is how tissues return to homeostasis after injury. Answering this question is essential for understanding the etiology of chronic disorders such as inflammatory bowel diseases and cancer. We used the Drosophila midgut to investigate this question and discovered that during regeneration a subpopulation of cholinergic enteric neurons triggers Ca2+ currents among enterocytes to promote return of the epithelium to homeostasis. Specifically, we found that down-regulation of the cholinergic enzyme Acetylcholinesterase in the epithelium enables acetylcholine from defined enteric neurons, referred as ARCENs, to activate nicotinic receptors in enterocytes found near ARCEN-innervations. This activation triggers high Ca2+ influx that spreads in the epithelium through Inx2/Inx7 gap junctions promoting enterocyte maturation followed by reduction of proliferation and inflammation. Disrupting this process causes chronic injury consisting of ion imbalance, Yki activation and increase of inflammatory cytokines together with hyperplasia, reminiscent of inflammatory bowel diseases. Altogether, we found that during gut regeneration the conserved cholinergic pathway facilitates epithelial Ca2+ waves that heal the intestinal epithelium. Our findings demonstrate nerve- and bioelectric-dependent intestinal regeneration which advance the current understanding of how a tissue returns to its homeostatic state after injury and could ultimately help existing therapeutics.

Circuits for self-motion estimation and walking control in Drosophila

The brain's evolution and operation are inextricably linked to animal movement, and critical functions, such as motor control, spatial perception, and navigation, rely on precise knowledge of body movement. Such internal estimates of self-motion emerge from the integration of mechanosensory and visual feedback with motor-related signals. Thus, this internal representation likely depends on the activity of circuits distributed across the central nervous system. However, the circuits responsible for self-motion estimation, and the exact mechanisms by which motor-sensory coordination occurs within these circuits remain poorly understood. Recent technological advances have positioned Drosophila melanogaster as an advantageous model for investigating the emergence, maintenance, and utilization of self-motion representations during naturalistic walking behaviors. In this review, I will illustrate how the adult fly is providing insights into the fundamental problems of self-motion computations and walking control, which have relevance for all animals.

Neural mechanisms to incorporate visual counterevidence in self motion estimation

In selecting appropriate behaviors, animals should weigh sensory evidence both for and against specific beliefs about the world. For instance, animals measure optic flow to estimate and control their own rotation. However, existing models of flow detection can confuse the movement of external objects with genuine self motion. Here, we show that stationary patterns on the retina, which constitute negative evidence against self rotation, are used by the fruit fly Drosophila to suppress inappropriate stabilizing rotational behavior. In silico experiments show that artificial neural networks optimized to distinguish self and world motion similarly detect stationarity and incorporate negative evidence. Employing neural measurements and genetic manipulations, we identified components of the circuitry for stationary pattern detection, which runs parallel to the fly's motion- and optic flow-detectors. Our results exemplify how the compact brain of the fly incorporates negative evidence to improve heading stability, exploiting geometrical constraints of the visual world.

Neuronal wiring diagram of an adult brain

Connections between neurons can be mapped by acquiring and analyzing electron microscopic (EM) brain images. In recent years, this approach has been applied to chunks of brains to reconstruct local connectivity maps that are highly informative, yet inadequate for understanding brain function more globally. Here, we present the first neuronal wiring diagram of a whole adult brain, containing 5×107 chemical synapses between ~130,000 neurons reconstructed from a female Drosophila melanogaster. The resource also incorporates annotations of cell classes and types, nerves, hemilineages, and predictions of neurotransmitter identities. Data products are available by download, programmatic access, and interactive browsing and made interoperable with other fly data resources. We show how to derive a projectome, a map of projections between regions, from the connectome. We demonstrate the tracing of synaptic pathways and the analysis of information flow from inputs (sensory and ascending neurons) to outputs (motor, endocrine, and descending neurons), across both hemispheres, and between the central brain and the optic lobes. Tracing from a subset of photoreceptors all the way to descending motor pathways illustrates how structure can uncover putative circuit mechanisms underlying sensorimotor behaviors. The technologies and open ecosystem of the FlyWire Consortium set the stage for future large-scale connectome projects in other species.

A rise-to-threshold process for a relative-value decision

Whereas progress has been made in the identification of neural signals related to rapid, cued decisions1-3, less is known about how brains guide and terminate more ethologically relevant decisions in which an animal's own behaviour governs the options experienced over minutes4-6. Drosophila search for many seconds to minutes for egg-laying sites with high relative value7,8 and have neurons, called oviDNs, whose activity fulfills necessity and sufficiency criteria for initiating the egg-deposition motor programme9. Here we show that oviDNs express a calcium signal that (1) dips when an egg is internally prepared (ovulated), (2) drifts up and down over seconds to minutes-in a manner influenced by the relative value of substrates-as a fly determines whether to lay an egg and (3) reaches a consistent peak level just before the abdomen bend for egg deposition. This signal is apparent in the cell bodies of oviDNs in the brain and it probably reflects a behaviourally relevant rise-to-threshold process in the ventral nerve cord, where the synaptic terminals of oviDNs are located and where their output can influence behaviour. We provide perturbational evidence that the egg-deposition motor programme is initiated once this process hits a threshold and that subthreshold variation in this process regulates the time spent considering options and, ultimately, the choice taken. Finally, we identify a small recurrent circuit that feeds into oviDNs and show that activity in each of its constituent cell types is required for laying an egg. These results argue that a rise-to-threshold process regulates a relative-value, self-paced decision and provide initial insight into the underlying circuit mechanism for building this process.

Predictive saccades and decision making in the beetle-predating saffron robber fly

Internal predictions about the sensory consequences of self-motion, encoded by corollary discharge, are ubiquitous in the animal kingdom, including for fruit flies, dragonflies, and humans. In contrast, predicting the future location of an independently moving external target requires an internal model. With the use of internal models for predictive gaze control, vertebrate predatory species compensate for their sluggish visual systems and long sensorimotor latencies. This ability is crucial for the timely and accurate decisions that underpin a successful attack. Here, we directly demonstrate that the robber fly Laphria saffrana, a specialized beetle predator, also uses predictive gaze control when head tracking potential prey. Laphria uses this predictive ability to perform the difficult categorization and perceptual decision task of differentiating a beetle from other flying insects with a low spatial resolution retina. Specifically, we show that (1) this predictive behavior is part of a saccade-and-fixate strategy, (2) the relative target angular position and velocity, acquired during fixation, inform the subsequent predictive saccade, and (3) the predictive saccade provides Laphria with additional fixation time to sample the frequency of the prey's specular wing reflections. We also demonstrate that Laphria uses such wing reflections as a proxy for the wingbeat frequency of the potential prey and that consecutively flashing LEDs to produce apparent motion elicits attacks when the LED flicker frequency matches that of the beetle's wingbeat cycle.

The self as part of the sensory ecology: how behavior affects sensation from the inside out

Insects exhibit remarkable sensory and motor capabilities to successfully navigate their environment. As insects move, they activate sensory afferents. Hence, insects are inextricably part of their sensory ecology. Insects must correctly attribute self- versus external sources of sensory activation to make adaptive behavioral choices. This is achieved via corollary discharge circuits (CDCs), motor-to-sensory neuronal pathways providing predictive motor signals to sensory networks to coordinate sensory processing within the context of ongoing behavior. While CDCs provide predictive motor signals, their underlying mechanisms of action and functional consequences are diverse. Here, we describe inferred CDCs and identified corollary discharge interneurons (CDIs) in insects, highlighting their anatomical commonalities and our limited understanding of their synaptic integration into the nervous system. By using connectomics information, we demonstrate that the complexity with which identified CDIs integrate into the central nervous system (CNS) can be revealed.

Visual processing in the fly, from photoreceptors to behavior

Originally a genetic model organism, the experimental use of Drosophila melanogaster has grown to include quantitative behavioral analyses, sophisticated perturbations of neuronal function, and detailed sensory physiology. A highlight of these developments can be seen in the context of vision, where pioneering studies have uncovered fundamental and generalizable principles of sensory processing. Here we begin with an overview of vision-guided behaviors and common methods for probing visual circuits. We then outline the anatomy and physiology of brain regions involved in visual processing, beginning at the sensory periphery and ending with descending motor control. Areas of focus include contrast and motion detection in the optic lobe, circuits for visual feature selectivity, computations in support of spatial navigation, and contextual associative learning. Finally, we look to the future of fly visual neuroscience and discuss promising topics for further study.

A deep learning analysis of Drosophila body kinematics during magnetically tethered flight

Flying Drosophila rely on their vision to detect visual objects and adjust their flight course. Despite their robust fixation on a dark, vertical bar, our understanding of the underlying visuomotor neural circuits remains limited, in part due to difficulties in analyzing detailed body kinematics in a sensitive behavioral assay. In this study, we observed the body kinematics of flying Drosophila using a magnetically tethered flight assay, in which flies are free to rotate around their yaw axis, enabling naturalistic visual and proprioceptive feedback. Additionally, we used deep learning-based video analyses to characterize the kinematics of multiple body parts in flying animals. By applying this pipeline of behavioral experiments and analyses, we characterized the detailed body kinematics during rapid flight turns (or saccades) in two different visual conditions: spontaneous flight saccades under static screen and bar-fixating saccades while tracking a rotating bar. We found that both types of saccades involved movements of multiple body parts and that the overall dynamics were comparable. Our study highlights the importance of sensitive behavioral assays and analysis tools for characterizing complex visual behaviors.

Long timescale anti-directional rotation in Drosophila optomotor behavior

Locomotor movements cause visual images to be displaced across the eye, a retinal slip that is counteracted by stabilizing reflexes in many animals. In insects, optomotor turning causes the animal to turn in the direction of rotating visual stimuli, thereby reducing retinal slip and stabilizing trajectories through the world. This behavior has formed the basis for extensive dissections of motion vision. Here, we report that under certain stimulus conditions, two Drosophila species, including the widely studied D. melanogaster, can suppress and even reverse the optomotor turning response over several seconds. Such "anti-directional turning" is most strongly evoked by long-lasting, high-contrast, slow-moving visual stimuli that are distinct from those that promote syn-directional optomotor turning. Anti-directional turning, like the syn-directional optomotor response, requires the local motion detecting neurons T4 and T5. A subset of lobula plate tangential cells, CH cells, show involvement in these responses. Imaging from a variety of direction-selective cells in the lobula plate shows no evidence of dynamics that match the behavior, suggesting that the observed inversion in turning direction emerges downstream of the lobula plate. Further, anti-directional turning declines with age and exposure to light. These results show that Drosophila optomotor turning behaviors contain rich, stimulus-dependent dynamics that are inconsistent with simple reflexive stabilization responses.

A visuomotor circuit for evasive flight turns in Drosophila

Visual systems extract multiple features from a scene using parallel neural circuits. Ultimately, the separate neural signals must come together to coherently influence action. Here, we characterize a circuit in Drosophila that integrates multiple visual features related to imminent threats to drive evasive locomotor turns. We identified, using genetic perturbation methods, a pair of visual projection neurons (LPLC2) and descending neurons (DNp06) that underlie evasive flight turns in response to laterally moving or approaching visual objects. Using two-photon calcium imaging or whole-cell patch clamping, we show that these cells indeed respond to both translating and approaching visual patterns. Furthermore, by measuring visual responses of LPLC2 neurons after genetically silencing presynaptic motion-sensing neurons, we show that their visual properties emerge by integrating multiple visual features across two early visual structures: the lobula and the lobula plate. This study highlights a clear example of how distinct visual signals converge on a single class of visual neurons and then activate premotor neurons to drive action, revealing a concise visuomotor pathway for evasive flight maneuvers in Drosophila.

Muscles that move the retina augment compound eye vision in Drosophila

Most animals have compound eyes, with tens to thousands of lenses attached rigidly to the exoskeleton. A natural assumption is that all of these species must resort to moving either their head or their body to actively change their visual input. However, classic anatomy has revealed that flies have muscles poised to move their retinas under the stable lenses of each compound eye1-3. Here we show that Drosophila use their retinal muscles to smoothly track visual motion, which helps to stabilize the retinal image, and also to perform small saccades when viewing a stationary scene. We show that when the retina moves, visual receptive fields shift accordingly, and that even the smallest retinal saccades activate visual neurons. Using a head-fixed behavioural paradigm, we find that Drosophila perform binocular, vergence movements of their retinas-which could enhance depth perception-when crossing gaps, and impairing the physiology of retinal motor neurons alters gap-crossing trajectories during free behaviour. That flies evolved an ability to actuate their retinas suggests that moving the eye independently of the head is broadly paramount for animals. The similarities of smooth and saccadic movements of the Drosophila retina and the vertebrate eye highlight a notable example of convergent evolution.

Visual and motor signatures of locomotion dynamically shape a population code for feature detection in Drosophila

Natural vision is dynamic: as an animal moves, its visual input changes dramatically. How can the visual system reliably extract local features from an input dominated by self-generated signals? In Drosophila, diverse local visual features are represented by a group of projection neurons with distinct tuning properties. Here, we describe a connectome-based volumetric imaging strategy to measure visually evoked neural activity across this population. We show that local visual features are jointly represented across the population, and a shared gain factor improves trial-to-trial coding fidelity. A subset of these neurons, tuned to small objects, is modulated by two independent signals associated with self-movement, a motor-related signal, and a visual motion signal associated with rotation of the animal. These two inputs adjust the sensitivity of these feature detectors across the locomotor cycle, selectively reducing their gain during saccades and restoring it during intersaccadic intervals. This work reveals a strategy for reliable feature detection during locomotion.

Matched function of the neuropil processing optic flow in flies and crabs: the lobula plate mediates optomotor responses in Neohelice granulata

When an animal rotates (whether it is an arthropod, a fish, a bird or a human) a drift of the visual panorama occurs over its retina, termed optic flow. The image is stabilized by compensatory behaviours (driven by the movement of the eyes, head or the whole body depending on the animal) collectively termed optomotor responses. The dipteran lobula plate has been consistently linked with optic flow processing and the control of optomotor responses. Crabs have a neuropil similarly located and interconnected in the optic lobes, therefore referred to as a lobula plate too. Here we show that the crabs' lobula plate is required for normal optomotor responses since the response was lost or severely impaired in animals whose lobula plate had been lesioned. The effect was behaviour-specific, since avoidance responses to approaching visual stimuli were not affected. Crabs require simpler optic flow processing than flies (because they move slower and in two-dimensional instead of three-dimensional space), consequently their lobula plates are relatively smaller. Nonetheless, they perform the same essential role in the visual control of behaviour. Our findings add a fundamental piece to the current debate on the evolutionary relationship between the lobula plates of insects and crustaceans.

The emergence and influence of internal states

Animal behavior is shaped by a variety of "internal states"-partially hidden variables that profoundly shape perception, cognition, and action. The neural basis of internal states, such as fear, arousal, hunger, motivation, aggression, and many others, is a prominent focus of research efforts across animal phyla. Internal states can be inferred from changes in behavior, physiology, and neural dynamics and are characterized by properties such as pleiotropy, persistence, scalability, generalizability, and valence. To date, it remains unclear how internal states and their properties are generated by nervous systems. Here, we review recent progress, which has been driven by advances in behavioral quantification, cellular manipulations, and neural population recordings. We synthesize research implicating defined subsets of state-inducing cell types, widespread changes in neural activity, and neuromodulation in the formation and updating of internal states. In addition to highlighting the significance of these findings, our review advocates for new approaches to clarify the underpinnings of internal brain states across the animal kingdom.

Neuronal circuits integrating visual motion information in Drosophila melanogaster

The detection of visual motion enables sophisticated animal navigation, and studies on flies have provided profound insights into the cellular and circuit bases of this neural computation. The fly's directionally selective T4 and T5 neurons encode ON and OFF motion, respectively. Their axons terminate in one of the four retinotopic layers in the lobula plate, where each layer encodes one of the four directions of motion. Although the input circuitry of the directionally selective neurons has been studied in detail, the synaptic connectivity of circuits integrating T4/T5 motion signals is largely unknown. Here, we report a 3D electron microscopy reconstruction, wherein we comprehensively identified T4/T5's synaptic partners in the lobula plate, revealing a diverse set of new cell types and attributing new connectivity patterns to the known cell types. Our reconstruction explains how the ON- and OFF-motion pathways converge. T4 and T5 cells that project to the same layer connect to common synaptic partners and comprise a core motif together with bilayer interneurons, detailing the circuit basis for computing motion opponency. We discovered pathways that likely encode new directions of motion by integrating vertical and horizontal motion signals from upstream T4/T5 neurons. Finally, we identify substantial projections into the lobula, extending the known motion pathways and suggesting that directionally selective signals shape feature detection there. The circuits we describe enrich the anatomical basis for experimental and computations analyses of motion vision and bring us closer to understanding complete sensory-motor pathways.

Impact of walking speed and motion adaptation on optokinetic nystagmus-like head movements in the blowfly Calliphora

The optokinetic nystagmus is a gaze-stabilizing mechanism reducing motion blur by rapid eye rotations against the direction of visual motion, followed by slower syndirectional eye movements minimizing retinal slip speed. Flies control their gaze through head turns controlled by neck motor neurons receiving input directly, or via descending neurons, from well-characterized directional-selective interneurons sensitive to visual wide-field motion. Locomotion increases the gain and speed sensitivity of these interneurons, while visual motion adaptation in walking animals has the opposite effects. To find out whether flies perform an optokinetic nystagmus, and how it may be affected by locomotion and visual motion adaptation, we recorded head movements of blowflies on a trackball stimulated by progressive and rotational visual motion. Flies flexibly responded to rotational stimuli with optokinetic nystagmus-like head movements, independent of their locomotor state. The temporal frequency tuning of these movements, though matching that of the upstream directional-selective interneurons, was only mildly modulated by walking speed or visual motion adaptation. Our results suggest flies flexibly control their gaze to compensate for rotational wide-field motion by a mechanism similar to an optokinetic nystagmus. Surprisingly, the mechanism is less state-dependent than the response properties of directional-selective interneurons providing input to the neck motor system.

Walking strides direct rapid and flexible recruitment of visual circuits for course control in Drosophila

Flexible mapping between activity in sensory systems and movement parameters is a hallmark of motor control. This flexibility depends on the continuous comparison of short-term postural dynamics and the longer-term goals of an animal, thereby necessitating neural mechanisms that can operate across multiple timescales. To understand how such body-brain interactions emerge across timescales to control movement, we performed whole-cell patch recordings from visual neurons involved in course control in Drosophila. We show that the activity of leg mechanosensory cells, propagating via specific ascending neurons, is critical for stride-by-stride steering adjustments driven by the visual circuit, and, at longer timescales, it provides information about the moving body’s state to flexibly recruit the visual circuit for course control. Thus, our findings demonstrate the presence of an elegant stride-based mechanism operating at multiple timescales for context-dependent course control. We propose that this mechanism functions as a general basis for the adaptive control of locomotion.

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HS cells receive stride-coupled signals via ascending neurons

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The stride-coupled signals reflect an internal motor context

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Motor context modulates HS cells at multiple timescales

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HS cells drive rapid steering depending on motor context

From Photons to Behaviors: Neural Implementations of Visual Behaviors in Drosophila

Neural implementations of visual behaviors in Drosophila have been dissected intensively in the past couple of decades. The availability of premiere genetic toolkits, behavioral assays in tethered or freely moving conditions, and advances in connectomics have permitted the understanding of the physiological and anatomical details of the nervous system underlying complex visual behaviors. In this review, we describe recent advances on how various features of a visual scene are detected by the Drosophila visual system and how the neural circuits process these signals and elicit an appropriate behavioral response. Special emphasis was laid on the neural circuits that detect visual features such as brightness, color, local motion, optic flow, and translating or approaching visual objects, which would be important for behaviors such as phototaxis, optomotor response, attraction (or aversion) to moving objects, navigation, and visual learning. This review offers an integrative framework for how the fly brain detects visual features and orchestrates an appropriate behavioral response.

Dragonfly Neurons Selectively Attend to Targets Within Natural Scenes

Aerial predators, such as the dragonfly, determine the position and movement of their prey even when both are moving through complex, natural scenes. This task is likely supported by a group of neurons in the optic lobe which respond to moving targets that subtend less than a few degrees. These Small Target Motion Detector (STMD) neurons are tuned to both target size and velocity, whilst also exhibiting facilitated responses to targets traveling along continuous trajectories. When presented with a pair of targets, some STMDs generate spiking activity that represent a competitive selection of one target, as if the alternative does not exist (i.e., selective attention). Here, we describe intracellular responses of CSTMD1 (an identified STMD) to the visual presentation of targets embedded within cluttered, natural scenes. We examine CSTMD1 response changes to target contrast, as well as a range of target and background velocities. We find that background motion affects CSTMD1 responses via the competitive selection between features within the natural scene. Here, robust discrimination of our artificially embedded “target” is limited to scenarios when its velocity is matched to, or greater than, the background velocity. Additionally, the background’s direction of motion affects discriminability, though not in the manner observed in STMDs of other flying insects. Our results highlight that CSTMD1’s competitive responses are to those features best matched to the neuron’s underlying spatiotemporal tuning, whether from the embedded target or other features in the background clutter. In many scenarios, CSTMD1 responds robustly to targets moving through cluttered scenes. However, whether this neuronal system could underlie the task of competitively selecting slow moving prey against fast-moving backgrounds remains an open question.

Populations of local direction-selective cells encode global motion patterns generated by self-motion

Self-motion generates visual patterns on the eye that are important for navigation. These optic flow patterns are encoded by the population of local direction–selective cells in the mouse retina, whereas in flies, local direction–selective T4/T5 cells are thought to be uniformly tuned. How complex global motion patterns can be computed downstream is unclear. We show that the population of T4/T5 cells in Drosophila encodes global motion patterns. Whereas the mouse retina encodes four types of optic flow, the fly visual system encodes six. This matches the larger number of degrees of freedom and the increased complexity of translational and rotational motion patterns during flight. The four uniformly tuned T4/T5 subtypes described previously represent a local subset of the population. Thus, a population code for global motion patterns appears to be a general coding principle of visual systems that matches local motion responses to modes of the animal’s movement.

Neural Circuits Underlying Behavioral Flexibility: Insights From Drosophila

Behavioral flexibility is critical to survival. Animals must adapt their behavioral responses based on changes in the environmental context, internal state, or experience. Studies in Drosophila melanogaster have provided insight into the neural circuit mechanisms underlying behavioral flexibility. Here we discuss how Drosophila behavior is modulated by internal and behavioral state, environmental context, and learning. We describe general principles of neural circuit organization and modulation that underlie behavioral flexibility, principles that are likely to extend to other species.

Building an allocentric travelling direction signal via vector computation

Many behavioural tasks require the manipulation of mathematical vectors, but, outside of computational models1-7, it is not known how brains perform vector operations. Here we show how the Drosophila central complex, a region implicated in goal-directed navigation7-10, performs vector arithmetic. First, we describe a neural signal in the fan-shaped body that explicitly tracks the allocentric travelling angle of a fly, that is, the travelling angle in reference to external cues. Past work has identified neurons in Drosophila8,11-13 and mammals14 that track the heading angle of an animal referenced to external cues (for example, head direction cells), but this new signal illuminates how the sense of space is properly updated when travelling and heading angles differ (for example, when walking sideways). We then characterize a neuronal circuit that performs an egocentric-to-allocentric (that is, body-centred to world-centred) coordinate transformation and vector addition to compute the allocentric travelling direction. This circuit operates by mapping two-dimensional vectors onto sinusoidal patterns of activity across distinct neuronal populations, with the amplitude of the sinusoid representing the length of the vector and its phase representing the angle of the vector. The principles of this circuit may generalize to other brains and to domains beyond navigation where vector operations or reference-frame transformations are required.

A connectome of the Drosophila central complex reveals network motifs suitable for flexible navigation and context-dependent action selection

Flexible behaviors over long timescales are thought to engage recurrent neural networks in deep brain regions, which are experimentally challenging to study. In insects, recurrent circuit dynamics in a brain region called the central complex (CX) enable directed locomotion, sleep, and context- and experience-dependent spatial navigation. We describe the first complete electron-microscopy-based connectome of the Drosophila CX, including all its neurons and circuits at synaptic resolution. We identified new CX neuron types, novel sensory and motor pathways, and network motifs that likely enable the CX to extract the fly's head-direction, maintain it with attractor dynamics, and combine it with other sensorimotor information to perform vector-based navigational computations. We also identified numerous pathways that may facilitate the selection of CX-driven behavioral patterns by context and internal state. The CX connectome provides a comprehensive blueprint necessary for a detailed understanding of network dynamics underlying sleep, flexible navigation, and state-dependent action selection.

Fast tuning of posture control by visual feedback underlies gaze stabilization in walking Drosophila

Locomotion requires a balance between mechanical stability and movement flexibility to achieve behavioral goals despite noisy neuromuscular systems, but rarely is it considered how this balance is orchestrated. We combined virtual reality tools with quantitative analysis of behavior to examine how Drosophila uses self-generated visual information (reafferent visual feedback) to control gaze during exploratory walking. We found that flies execute distinct motor programs coordinated across the body to maximize gaze stability. However, the presence of inherent variability in leg placement relative to the body jeopardizes fine control of gaze due to posture-stabilizing adjustments that lead to unintended changes in course direction. Surprisingly, whereas visual feedback is dispensable for head-body coordination, we found that self-generated visual signals tune postural reflexes to rapidly prevent turns rather than to promote compensatory rotations, a long-standing idea for visually guided course control. Together, these findings support a model in which visual feedback orchestrates the interplay between posture and gaze stability in a manner that is both goal dependent and motor-context specific.

Suppression of motion vision during course-changing, but not course-stabilizing, navigational turns

From mammals to insects, locomotion has been shown to strongly modulate visual-system physiology. Does the manner in which a locomotor act is initiated change the modulation observed? We performed patch-clamp recordings from motion-sensitive visual neurons in tethered, flying Drosophila. We observed motor-related signals in flies performing flight turns in rapid response to looming discs and also during spontaneous turns, but motor-related signals were weak or non-existent in the context of turns made in response to brief pulses of unidirectional visual motion (i.e., optomotor responses). Thus, the act of a locomotor turn is variably associated with modulation of visual processing. These results can be understood via the following principle: suppress visual responses during course-changing, but not course-stabilizing, navigational turns. This principle is likely to apply broadly-even to mammals-whenever visual cells whose activity helps to stabilize a locomotor trajectory or the visual gaze angle are targeted for motor modulation.

Mechanisms of punctuated vision in fly flight

To guide locomotion, animals control gaze via movements of their eyes, head, and/or body, but how the nervous system controls gaze during complex motor tasks remains elusive. In many animals, shifts in gaze consist of periods of smooth movement punctuated by rapid eye saccades. Notably, eye movements are constrained by anatomical limits, which requires resetting eye position. By studying tethered, flying fruit flies (Drosophila), we show that flies perform stereotyped head saccades to reset gaze, analogous to optokinetic nystagmus in primates. Head-reset saccades interrupted head smooth movement for as little as 50 ms-representing less than 5% of the total flight time-thereby enabling punctuated gaze stabilization. By revealing the passive mechanics of the neck joint, we show that head-reset saccades leverage the neck's natural elastic recoil, enabling mechanically assisted redirection of gaze. The consistent head orientation at saccade initiation, the influence of the head's angular position on saccade rate, the decrease in wing saccade frequency in head-fixed flies, and the decrease in head-reset saccade rate in flies with their head range of motion restricted together implicate proprioception as the primary trigger of head-reset saccades. Wing-reset saccades were influenced by head orientation, establishing a causal link between neck sensory signals and the execution of body saccades. Head-reset saccades were abolished when flies switched to a landing state, demonstrating that head movements are gated by behavioral state. We propose a control architecture for active vision systems with limits in sensor range of motion. VIDEO ABSTRACT.

Aerial course stabilization is impaired in motion-blind flies

Visual motion detection is among the best understood neuronal computations. As extensively investigated in tethered flies, visual motion signals are assumed to be crucial to detect and counteract involuntary course deviations. During free flight, however, course changes are also signalled by other sensory systems. Therefore, it is as yet unclear to what extent motion vision contributes to course control. To address this question, we genetically rendered flies motion-blind by blocking their primary motion-sensitive neurons and quantified their free-flight performance. We found that such flies have difficulty maintaining a straight flight trajectory, much like unimpaired flies in the dark. By unilateral wing clipping, we generated an asymmetry in propulsive force and tested the ability of flies to compensate for this perturbation. While wild-type flies showed a remarkable level of compensation, motion-blind animals exhibited pronounced circling behaviour. Our results therefore directly confirm that motion vision is necessary to fly straight under realistic conditions.

Maximally efficient prediction in the early fly visual system may support evasive flight maneuvers

The visual system must make predictions to compensate for inherent delays in its processing. Yet little is known, mechanistically, about how prediction aids natural behaviors. Here, we show that despite a 20-30ms intrinsic processing delay, the vertical motion sensitive (VS) network of the blowfly achieves maximally efficient prediction. This prediction enables the fly to fine-tune its complex, yet brief, evasive flight maneuvers according to its initial ego-rotation at the time of detection of the visual threat. Combining a rich database of behavioral recordings with detailed compartmental modeling of the VS network, we further show that the VS network has axonal gap junctions that are critical for optimal prediction. During evasive maneuvers, a VS subpopulation that directly innervates the neck motor center can convey predictive information about the fly’s future ego-rotation, potentially crucial for ongoing flight control. These results suggest a novel sensory-motor pathway that links sensory prediction to behavior.

Survival-critical behaviors shape neural circuits to translate sensory information into strikingly fast predictions, e.g. in escaping from a predator faster than the system’s processing delay. We show that the fly visual system implements fast and accurate prediction of its visual experience. This provides crucial information for directing fast evasive maneuvers that unfold over just 40ms. Our work shows how this fast prediction is implemented, mechanistically, and suggests the existence of a novel sensory-motor pathway from the fly visual system to a wing steering motor neuron. Echoing and amplifying previous work in the retina, our work hypothesizes that the efficient encoding of predictive information is a universal design principle supporting fast, natural behaviors.

Stark trade-offs and elegant solutions in arthropod visual systems

Vision is one of the most important senses for humans and animals alike. Diverse elegant specializations have evolved among insects and other arthropods in response to specific visual challenges and ecological needs. These specializations are the subject of this Review, and they are best understood in light of the physical limitations of vision. For example, to achieve high spatial resolution, fine sampling in different directions is necessary, as demonstrated by the well-studied large eyes of dragonflies. However, it has recently been shown that a comparatively tiny robber fly (Holcocephala) has similarly high visual resolution in the frontal visual field, despite their eyes being a fraction of the size of those of dragonflies. Other visual specializations in arthropods include the ability to discern colors, which relies on parallel inputs that are tuned to spectral content. Color vision is important for detection of objects such as mates, flowers and oviposition sites, and is particularly well developed in butterflies, stomatopods and jumping spiders. Analogous to color vision, the visual systems of many arthropods are specialized for the detection of polarized light, which in addition to communication with conspecifics, can be used for orientation and navigation. For vision in low light, optical superposition compound eyes perform particularly well. Other modifications to maximize photon capture involve large lenses, stout photoreceptors and, as has been suggested for nocturnal bees, the neural pooling of information. Extreme adaptations even allow insects to see colors at very low light levels or to navigate using the Milky Way.

Haltere and visual inputs sum linearly to predict wing (but not gaze) motor output in tethered flying Drosophila

In the true flies (Diptera), the hind wings have evolved into specialized mechanosensory organs known as halteres, which are sensitive to gyroscopic and other inertial forces. Together with the fly's visual system, the halteres direct head and wing movements through a suite of equilibrium reflexes that are crucial to the fly's ability to maintain stable flight. As in other animals (including humans), this presents challenges to the nervous system as equilibrium reflexes driven by the inertial sensory system must be integrated with those driven by the visual system in order to control an overlapping pool of motor outputs shared between the two of them. Here, we introduce an experimental paradigm for reproducibly altering haltere stroke kinematics and use it to quantify multisensory integration of wing and gaze equilibrium reflexes. We show that multisensory wing-steering responses reflect a linear superposition of haltere-driven and visually driven responses, but that multisensory gaze responses are not well predicted by this framework. These models, based on populations, extend also to the responses of individual flies.

Timing precision in fly flight control: integrating mechanosensory input with muscle physiology

Animals rapidly collect and act on incoming information to navigate complex environments, making the precise timing of sensory feedback critical in the context of neural circuit function. Moreover, the timing of sensory input determines the biomechanical properties of muscles that undergo cyclic length changes, as during locomotion. Both of these issues come to a head in the case of flying insects, as these animals execute steering manoeuvres at timescales approaching the upper limits of performance for neuromechanical systems. Among insects, flies stand out as especially adept given their ability to execute manoeuvres that require sub-millisecond control of steering muscles. Although vision is critical, here I review the role of rapid, wingbeat-synchronous mechanosensory feedback from the wings and structures unique to flies, the halteres. The visual system and descending interneurons of the brain employ a spike rate coding scheme to relay commands to the wing steering system. By contrast, mechanosensory feedback operates at faster timescales and in the language of motor neurons, i.e. spike timing, allowing wing and haltere input to dynamically structure the output of the wing steering system. Although the halteres have been long known to provide essential input to the wing steering system as gyroscopic sensors, recent evidence suggests that the feedback from these vestigial hindwings is under active control. Thus, flies may accomplish manoeuvres through a conserved hindwing circuit, regulating the firing phase-and thus, the mechanical power output-of the wing steering muscles.

Hybrid visual control in fly flight: insights into gaze shift via saccades

Flies fly by alternating between periods of fixation and body saccades, analogous to how our own eyes move. Gaze fixation via smooth movement in fly flight has been studied extensively, but comparatively less is known about the mechanism by which flies trigger and control body saccades to shift their gaze. Why do flies implement a hybrid fixate-and-saccade locomotion strategy? Here we review recent developments that provide new insights into this question. We focus on the interplay between smooth movement and saccades, the trigger classes of saccades, and the timeline of saccade execution. We emphasize recent mechanistic advances in Drosophila, where genetic tools have enabled cellular circuit analysis at an unprecedented level in a flying insect. In addition, we review trade-offs in behavioral paradigms used to study saccades. Throughout we highlight exciting avenues for future research in the control of fly flight.

Active vision shapes and coordinates flight motor responses in flies

Animals use active sensing to respond to sensory inputs and guide future motor decisions. In flight, flies generate a pattern of head and body movements to stabilize gaze. How the brain relays visual information to control head and body movements and how active head movements influence downstream motor control remains elusive. Using a control theoretic framework, we studied the optomotor gaze stabilization reflex in tethered flight and quantified how head movements stabilize visual motion and shape wing steering efforts in fruit flies (Drosophila). By shaping visual inputs, head movements increased the gain of wing steering responses and coordination between stimulus and wings, pointing to a tight coupling between head and wing movements. Head movements followed the visual stimulus in as little as 10 ms-a delay similar to the human vestibulo-ocular reflex-whereas wing steering responses lagged by more than 40 ms. This timing difference suggests a temporal order in the flow of visual information such that the head filters visual information eliciting downstream wing steering responses. Head fixation significantly decreased the mechanical power generated by the flight motor by reducing wingbeat frequency and overall thrust. By simulating an elementary motion detector array, we show that head movements shift the effective visual input dynamic range onto the sensitivity optimum of the motion vision pathway. Taken together, our results reveal a transformative influence of active vision on flight motor responses in flies. Our work provides a framework for understanding how to coordinate moving sensors on a moving body.

Development of a Mouse Pain Scale Using Sub-second Behavioral Mapping and Statistical Modeling

Rodents are the main model systems for pain research, but determining their pain state is challenging. To develop an objective method to assess pain sensation in mice, we adopt high-speed videography to capture sub-second behavioral features following hind paw stimulation with both noxious and innocuous stimuli and identify several differentiating parameters indicating the affective and reflexive aspects of nociception. Using statistical modeling and machine learning, we integrate these parameters into a single index and create a "mouse pain scale," which allows us to assess pain sensation in a graded manner for each withdrawal. We demonstrate the utility of this method by determining sensations triggered by three different von Frey hairs and optogenetic activation of two different nociceptor populations. Our behavior-based "pain scale" approach will help improve the rigor and reproducibility of using withdrawal reflex assays to assess pain sensation in mice.

Brain-wide representations of ongoing behavior: a universal principle?

Recent neuronal activity recordings of unprecedented breadth and depth in worms, flies, and mice have uncovered a surprising common feature: brain-wide behavior-related signals. These signals pervade, and even dominate, neuronal populations thought to function primarily in sensory processing. Such convergent findings across organisms suggest that brain-wide representations of behavior might be a universal neuroscientific principle. What purpose(s) do these representations serve? Here we review these findings along with suggested functions, including sensory prediction, context-dependent sensory processing, and, perhaps most speculatively, distributed motor command generation. It appears that a large proportion of the brain's energy and coding capacity is used to represent ongoing behavior; understanding the function of these representations should therefore be a major goal in neuroscience research.